University of Veterinary Medicine Vienna - Research portal

Diagrammed Link to Homepage University of Veterinary Medicine, Vienna

Selected Publication:

Open Access Logo

Type of publication: Journal Article
Type of document: Review Article

Year: 2020

Authors: Walter, A; Paul-Gilloteaux, P; Plochberger, B; Sefc, L; Verkade, P; Mannheim, JG; Slezak, P; Unterhuber, A; Marchetti-Deschmann, M; Ogris, M; Bühler, K; Fixler, D; Geyer, SH; Weninger, WJ; Glösmann, M; Handschuh, S; Wanek, T

Title: Correlated Multimodal Imaging in Life Sciences: Expanding the Biomedical Horizon.

Source: Frontiers in Physics 2020; 8: 47

Authors Vetmeduni Vienna:

Glösmann Martin
Handschuh Stephan

Vetmed Research Units

The frontiers of bioimaging are currently being pushed toward the integration and correlation of several modalities to tackle biomedical research questions holistically and across multiple scales. Correlated Multimodal Imaging (CMI) gathers information about exactly the same specimen with two or more complementary modalities that-in combination-create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows to describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, diseases or organisms by untangling their molecular mechanisms within their native environment. The two best-established CMI implementations for small animals and model organisms are hardware-fused platforms in preclinical imaging (Hybrid Imaging) and Correlated Light and Electron Microscopy (CLEM) in biological imaging. Although the merits of Preclinical Hybrid Imaging (PHI) and CLEM are well-established, both approaches would benefit from standardization of protocols, ontologies and data handling, and the development of optimized and advanced implementations. Specifically, CMI pipelines that aim at bridging preclinical and biological imaging beyond CLEM and PHI are rare but bear great potential to substantially advance both bioimaging and biomedical research. CMI faces three main challenges for its routine use in biomedical research: (1) Sample handling and preparation procedures that are compatible across modalities without compromising data quality, (2) soft- and hardware solutions to relocate the same region of interest (ROI) after transfer between imaging platforms including fiducial markers, and (3) automated software solutions to correlate complex, multiscale, multimodal and volumetric image data including reconstruction, segmentation and visualization. This review goes beyond preclinical imaging and puts accessible information into a broader imaging context. We present a comprehensive overview of the field of CMI from preclinical hybrid imaging to correlative microscopy, highlight requirements for optimization and standardization, present a synopsis of current solutions to challenges of the field and focus on current efforts to bridge the gap between preclinical and biological imaging (from small animals down to single cells and molecules). The review is in line with major European initiatives, such as COMULIS (CA17121), a COST Action to promote and foster Correlated Multimodal Imaging in Life Sciences.

© University of Veterinary Medicine ViennaHelp and DownloadsAccessibility statement